A methodology for reducing the effect of meteorological parameters on a continuously recording gravity meter

Gravity changes in geodynamically active areas are usually monitored by using discrete measurements. The temporal resolution, which is only as good as the repeat rate of the observations (usually ranging between one month and one year), is the main drawback of such measurements. To achieve a better temporal resolution and thus improve the possibilities of the investigation task, continuous gravity measurements have to be performed. However, temperature and pressure fluctuations seriously affect the output of continuously running spring gravity meters. In this paper, a methodology allowing the signal from a recording gravity meter to be compensated for the effect of temperature and pressure is discussed. To model how the meter output depends on the interfering signals, both polynomial (LMS) and nonlinear (neuro-fuzzy) forms of three different model structures have been tested. The results obtained through the nonlinear algorithm are satisfactory and could constitute the background for the implementation of a real-time correction system using a fuzzy micro-controller chip